Reinforcement learning agents struggle with partial observability due to critic bias

A new analysis of reinforcement learning agents under partial observability reveals that learning performance suffers more than previously attributed to policy limitations. Researchers found that even when an optimal policy is representable and the value function is expressive, the learning algorithm can converge to a suboptimal policy. This occurs because the agent's inability to observe the full state leads the critic to misinterpret unexplained variations as sharp curvature in value estimates, guiding the actor away from the true optimum. The study suggests that adjusting the lookahead horizon for value estimates, rather than simply providing memory of past observations, is key to mitigating this issue. AI

IMPACT Identifies a fundamental limitation in current reinforcement learning algorithms, suggesting new avenues for improving agent performance in complex, partially observable environments.

RANK_REASON The cluster contains an academic paper detailing a theoretical analysis and experimental results for reinforcement learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

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